A delay-tolerant network caching method, system, device and medium based on a max-min ant colony algorithm

Abstract

A method, system, device, and medium for delay-tolerant network caching based on the minimax ant colony algorithm are disclosed. The method includes: first, classifying information; when a new message arrives, if the cache is not full, the new message is allowed into the cache; otherwise, the total pheromone concentration of the new message determines whether to allow it into the cache. Regardless of whether the new message is allowed into the cache, the pheromone concentration of similar messages in the cache is updated, and the updated pheromone concentration of similar messages is compared with a given pheromone concentration range. If the pheromone concentration does not exceed the range, caching ends; if it exceeds the range, the upper or lower limit of the given pheromone concentration range is used. The system, device, and medium are used to implement a delay-tolerant network caching method based on the minimax ant colony algorithm. This invention introduces the concept of pheromone concentration to determine whether the total pheromone concentration of a new message entering the cache is minimum, thus deciding whether to discard the new message. This has the characteristics of improving the message arrival rate in delay-tolerant networks and reducing network overhead.

Description

Technical Field

[0001] This invention relates to the field of delay-tolerant network caching technology, specifically to a delay-tolerant network caching method, system, device, and medium based on the max-min ant colony algorithm. Background Technology

[0002] With the development of science and technology and the continuous expansion of various new research fields, more and more complex and diverse new networks have emerged, such as military communication networks, wildlife detection networks, and interplanetary networks. These networks are characterized by limited network resources, difficulty in maintaining stable end-to-end links, and dynamic changes in network topology, forming a new type of network that differs from the traditional Internet, called Delay Tolerant Networks (DTN).

[0003] In recent years, there has been a lot of research on caching methods, such as:

[0004] (1) Discard the first message to enter the cache (DF): If the node's cache is full and a new message arrives, the DF algorithm will discard the first message to enter the cache.

[0005] (2) Random Discard (DR): If a node’s cache is full and a new message arrives, the DR algorithm will randomly discard the message in the cache.

[0006] (3) Discard the oldest message (DO): If the cache in a node is full and a new message arrives, the DO algorithm will discard the algorithm with the smallest remaining time to live.

[0007] The reason why the three discarding methods result in low message delivery rates and high network overhead when the cache is full is that they may discard important or soon-to-expire messages, thus affecting message delivery and processing efficiency. Furthermore, these methods may require nodes to continuously perform discarding operations or monitor message attributes, increasing network overhead.

[0008] A. Krifa uses global information in delay-tolerant networks to determine which information in the buffer will be discarded (KrifaA., Barakat C., Spyropoulos T.. Optimal Buffer Management Policies for DelayTolerant Networks[C]. Proc. of 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. Piscataway, NJ: IEEE, 2008: 260-268). However, since obtaining global information requires the current node to exchange information with other nodes, this method has the disadvantage of high network overhead.

[0009] Yong Zhang proposed a delay-tolerant network caching method based on a distributed caching strategy (Zhang Y. and Zhang T.. Cache Management Strategy Based on Distributed Storage in Delay / Disruption Tolerant Network[C]. Proc. of 2019 IEEE 19th International Conference on Communication Technology (ICCT). Piscataway, NJ: IEEE, 2019:1337-1341). This method utilizes the cache of communicable nodes to alleviate cache pressure. However, maintaining communicable nodes and their available caches requires information interaction with the current node, and the resulting interaction information increases the network overhead of the algorithm.

[0010] C. - N n u proposed a delay-tolerant network cache management method, "MaxDelivery" (C. - N n u. MaxDelivery: A New Approach to a DTN Buffer Management[C]. Proc. of 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM). Piscataway, NJ: IEEE, 2020: 60-61). This method effectively alleviates the buffer pressure in delay-tolerant networks, but the algorithm uses an ACK confirmation mechanism and tends to delete messages that have been copied many times, which will increase network overhead. Summary of the Invention

[0011] To overcome the shortcomings of the prior art, the present invention aims to provide a delay-tolerant network caching method, system, device, and medium based on the maximum-minimum ant colony algorithm. By determining whether the total pheromone concentration of a new message entering the cache is at its minimum, the invention decides whether to discard the new message. After each determination, the pheromone concentration of the same type of message in the cache is updated. This method improves the message arrival rate in the delay-tolerant network and reduces network overhead.

[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0013] A delay-tolerant network caching method based on the max-min ant colony algorithm specifically includes the following steps:

[0014] Step 1: Classify the information based on the initial node and the terminal node categories;

[0015] Step 2: When a new message arrives, check if the cache in the node is full.

[0016] If the cache is not full, let the new message enter the cache and proceed to step 5;

[0017] If the cache is full, proceed to step 3;

[0018] Step 3: Calculate the total pheromone concentration of new messages based on the ant colony algorithm;

[0019] Step 4: Compare the total pheromone concentration calculated in Step 3 with the total pheromone concentration of all messages in the current cache.

[0020] If the total pheromone concentration of the new message is at its minimum, discard the new message and proceed to step 5;

[0021] If the total pheromone concentration of the new message is not the minimum, then discard the message with the minimum total pheromone concentration in the current cache, allow the new message to enter the cache, and proceed to step 5.

[0022] Step 5: Based on the maximum-minimum ant colony algorithm and the node classification in Step 1, update the pheromone concentration of the same type of message in the cache according to the classification.

[0023] Step 6: Compare the updated pheromone concentration of similar messages from Step 5 with the given pheromone concentration range.

[0024] Within the scope: End the current caching, wait for the new message to arrive, and execute step 2 after the new message arrives;

[0025] Outside the range: When the upper limit is exceeded, the pheromone concentration of the updated message of the same type is taken as the upper limit of the given pheromone concentration range value; otherwise, the lower limit of the given pheromone concentration range value is taken, and the current caching ends, waiting for the new message to arrive. After the new message arrives, step 2 is executed.

[0026] Step 1 specifically includes:

[0027] During network initialization, all nodes in the entire network are divided into N categories, and information is divided into N*N categories according to the difference between the starting node and the target node, where N is the number of node categories in the network based on node attributes, and N is an integer greater than 1.

[0028] The node attributes include: node movement speed, node communication range, node movement model, and node communication speed.

[0029] Step 2 specifically includes:

[0030] When a new message arrives, compare the size of the new message with the remaining cache size of the node. If the size of the new message is greater than the remaining cache size of the node, the cache is considered full and step 3 is executed; otherwise, the node is considered not full, the new message is allowed to enter the cache, and step 5 is executed.

[0031] The calculation of the total pheromone concentration of the message in step 3 is specifically as follows:

[0032] (1)

[0033] (2)

[0034] (3)

[0035] in, This represents the total pheromone concentration of the i-th message at time t. express Entering the node at any time The The pheromone concentration of each message Indicate Entering the node at any time The The remaining lifetime of the message. Indicate Entering the node at any time The The number of times a message is forwarded. For cache utilization, Representing the The size of a message Representative node cache size, This represents the pheromone concentration of similar messages at time t. Recorded in node The cache table is initially set to 0 and then updated in step 5.

[0036] The cache occupancy rate is the ratio of the message size to the cache size of the arriving node.

[0037] Step 4 specifically includes the following steps:

[0038] Step 4.1: Use quicksort to sort the nodes. The messages are sorted according to their total pheromone concentration;

[0039] Step 4.2: If the total pheromone concentration of the new message is less than the total pheromone concentration of the smallest message after sorting in Step 4.1, discard the new message and proceed to Step 5; if the total pheromone concentration of the new message is greater than the total pheromone concentration of the smallest message after sorting, discard the message with the smallest total pheromone concentration in the current cache, allow the new message to enter the cache, and proceed to Step 5.

[0040] Step 5 specifically includes:

[0041] Based on the maximum-minimum ant colony algorithm, update the pheromone concentration of similar messages in the cache:

[0042] (4)

[0043] in, This represents the pheromone concentration of similar messages at time t. The decay coefficient representing the historical pheromone concentration. This represents the number of messages of the same category that enter the node at time t. This indicates the lower limit of the pheromone concentration range. This represents the upper limit of the pheromone concentration range, meaning that the pheromone concentration of the same type of message at the current moment is equal to the decrease in the pheromone concentration of the same type of message at the previous moment, plus the sum of the pheromone concentration passing through at the current moment.

[0044] Step 6 specifically includes:

[0045] The pheromone concentration is limited to a certain range, specifically:

[0046] When the lower limit of the given pheromone concentration range is ≤ When the value is less than or equal to the upper limit of the given pheromone concentration range, the current caching ends, and the system waits for a new message to arrive. Once the new message arrives, step 2 is executed.

[0047] when When the lower limit of a given pheromone concentration range is reached, Set the lower limit of the given pheromone concentration range, end the current caching, wait for a new message to arrive, and execute step 2 after the new message arrives;

[0048] when When given the upper limit of the pheromone concentration range, Set the upper limit of the given pheromone concentration range, end the current caching, wait for a new message to arrive, and execute step 2 after the new message arrives.

[0049] A delay-tolerant network caching system based on the maximum-minimum ant colony algorithm is implemented using a delay-tolerant network caching method based on the maximum-minimum ant colony algorithm, including:

[0050] Information classification module: Used to classify information based on the initial node and the end node category;

[0051] Pheromone concentration calculation module: Calculates the pheromone concentration of a single message, the pheromone concentration of messages of the same type, and the total pheromone concentration of messages, and limits them to a specific range, and is responsible for updating the pheromone concentration of messages of the same type in the node;

[0052] Cache management module: Manages the cache in the node, including:

[0053] Determine if the node cache is full to decide whether new messages should be directly entered into the cache or discarded after priority calculation before entering the cache;

[0054] The total pheromone concentration of the messages is determined to decide whether to discard new messages or existing messages.

[0055] A delay-tolerant network caching device based on the max-min ant colony algorithm, comprising:

[0056] Memory: Used to store the computer program that implements the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm;

[0057] Processor: Used to implement the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm when executing the computer program.

[0058] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a delay-tolerant network caching method based on the max-min ant colony algorithm.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. This invention improves the efficiency of subscription-based message networks by using a message classification mechanism that enables the delay-tolerant network caching mechanism to combine the characteristics of the messages themselves.

[0061] 2. This invention achieves higher message delivery rate and lower network overhead by randomly classifying messages according to the arrival nodes and using the ant colony algorithm.

[0062] 3. This invention uses the minimax ant colony algorithm, which makes it easier for the algorithm to escape local optima and improves network utilization.

[0063] In summary, compared with existing technologies, this invention, by randomly classifying messages according to their arrival nodes and utilizing ant colony algorithms and maximum-minimum ant colony algorithms, combined with the characteristics of the messages themselves, can improve the efficiency of subscription-based message networks and achieve the effects of low network overhead and high network utilization. Attached Figure Description

[0064] Figure 1 This is a flowchart of the steps of the present invention.

[0065] Figure 2 This is a graph showing the relationship between message arrival rate and cache size in this invention.

[0066] Figure 3 This is a graph showing the relationship between network overhead and cache size in this invention.

[0067] Figure 4 This is a graph showing the relationship between message arrival rate and transmission bandwidth in this invention.

[0068] Figure 5 This is a diagram showing the relationship between network overhead and transmission bandwidth in this invention.

[0069] Figure 6 This is a graph showing the message arrival rate over time according to the present invention.

[0070] Figure 7 This is a graph showing the network overhead over time according to the present invention. Detailed Implementation

[0071] The present invention will now be described in detail with reference to the accompanying drawings.

[0072] See Figure 1 A delay-tolerant network caching method based on the max-min ant colony algorithm specifically includes the following steps:

[0073] Step 1: First, randomly divide all nodes in the entire network into four categories, and then divide the information into a total of 16 categories according to the different types of starting and ending nodes;

[0074] Step 2: When a new message arrives, check if the cache in the node is full.

[0075] If the cache is not full, let the new message enter the cache and proceed to step 5;

[0076] If the cache is full, proceed to step 3;

[0077] Step 3: Calculate the total pheromone concentration of new messages based on the ant colony algorithm;

[0078] Step 4: Compare the total pheromone concentration calculated in Step 3 with the total pheromone concentration of all messages in the current cache.

[0079] If the total pheromone concentration of the new message is at its minimum, discard the new message and proceed to step 5;

[0080] If the total pheromone concentration of the new message is not the minimum, then discard the message with the minimum total pheromone concentration in the current cache, allow the new message to enter the cache, and proceed to step 5.

[0081] Step 5: Based on the maximum-minimum ant colony algorithm and the node classification in Step 1, update the pheromone concentration of the same type of message in the cache according to the classification.

[0082] Step 6: Compare the updated pheromone concentration of similar messages from Step 5 with the given pheromone concentration range.

[0083] Within the scope: End the current caching, wait for the new message to arrive, and execute step 2 after the new message arrives;

[0084] Outside the range: When the upper limit is exceeded, the pheromone concentration of the updated message of the same type is taken as the upper limit of the given pheromone concentration range value; otherwise, the lower limit of the given pheromone concentration range value is taken, and the current caching ends, waiting for the new message to arrive. After the new message arrives, step 2 is executed.

[0085] Step 1 specifically includes:

[0086] During network initialization, all nodes in the entire network are divided into four categories: A, B, C, and D, based on node movement speed, node communication range, node movement model, and node communication speed. Information is also divided into sixteen categories: AA, AB, AC, AD, BA, BB, BC, BD, CA, CB, CC, CD, DA, DB, DC, and DD, depending on the originating node and the destination node.

[0087] Step 2 specifically includes:

[0088] When a new message arrives, compare the size of the new message with the remaining cache size of the node. If the size of the new message is greater than the remaining cache size of the node, the cache is considered full and step 3 is executed; otherwise, the node is considered not full, the new message is allowed to enter the cache, and step 5 is executed.

[0089] The calculation of the total pheromone concentration of the message in step 3 is specifically as follows:

[0090] (5)

[0091] (6)

[0092] (7)

[0093] in, This represents the total pheromone concentration of the i-th message at time t. express Entering the node at any time The The pheromone concentration of each message Indicate Entering the node at any time The The remaining lifetime of the message. Indicate Entering the node at any time The The number of times a message is forwarded. For cache utilization, Representing the The size of a message Representative node cache size, This represents the pheromone concentration of similar messages at time t. Recorded in node The cache table is initially set to 0 and is updated in step 5.

[0094] The cache occupancy rate is the ratio of the message size to the cache size of the arriving node.

[0095] Step 4 specifically includes:

[0096] Step 4.1: Use quicksort to sort the nodes. The messages are sorted according to their total pheromone concentration;

[0097] Step 4.2: If the total pheromone concentration of the new message is less than the total pheromone concentration of the smallest message after sorting in Step 4.1, then discard the new message and proceed to Step 5; if the total pheromone concentration of the new message is greater than the total pheromone concentration of the smallest message after sorting, then discard the message with the smallest total pheromone concentration in the current cache, allow the new message to enter the cache, and proceed to Step 5.

[0098] Step 5 specifically includes:

[0099] Update the pheromone concentration of similar messages in the cache according to the max-min ant colony algorithm:

[0100] (8)

[0101] in, This represents the pheromone concentration of similar messages at time t. The decay coefficient representing the historical pheromone concentration. This represents the number of messages of the same category that enter the node at time t. This indicates the lower limit of the pheromone concentration range. This represents the upper limit of the pheromone concentration range, meaning that the pheromone concentration of the same type of message at the current moment is equal to the decrease in the pheromone concentration of the same type of message at the previous moment, plus the sum of the pheromone concentration passing through at the current moment.

[0102] Step 6 specifically includes:

[0103] when When the time comes, end the current caching and wait for the new message to arrive. Once the new message arrives, proceed to step 2.

[0104] when At that time, Set the value to 0.6 and end the current caching. Wait for a new message to arrive. Once the new message arrives, proceed to step 2.

[0105] when At that time, Set the value to 1.4 and end the current caching. Wait for a new message to arrive. Once the new message arrives, proceed to step 2.

[0106] Wherein, 0.6 is the lower limit of the given pheromone concentration range, and 1.4 is the upper limit of the given pheromone concentration range. Both 0.6 and 1.4 are the relatively optimal parameter values ​​obtained in the experiment. The parameter values ​​were selected by conducting 10,000 experiments and analyzing and screening the results to remove larger and smaller parameter values ​​in order to obtain more stable and reliable results.

[0107] A delay-tolerant network caching system based on the maximum-minimum ant colony algorithm is implemented using a delay-tolerant network caching method based on the maximum-minimum ant colony algorithm, including:

[0108] Information classification module: Classifies information based on the initial node and the terminal node category, thus implementing step 1 of the delay-tolerant network caching method based on the maximum min-ant colony algorithm in this invention;

[0109] The pheromone concentration calculation module calculates the pheromone concentration of a single message, the pheromone concentration of messages of the same type, and the total pheromone concentration of messages, and restricts them within a specific range. It is also responsible for updating the pheromone concentration of messages of the same type in the node, which is used to implement steps 3, 5, and 6 of the delay-tolerant network caching method based on the max-min ant colony algorithm in this invention.

[0110] Cache management module: Manages the cache in the node, including:

[0111] Determine if the node cache is full to decide whether new messages should be directly entered into the cache or discarded after priority calculation before entering the cache;

[0112] The total pheromone concentration of the messages is determined to decide whether to discard new messages or existing messages.

[0113] Steps 2 and 4 are used to implement the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm in this invention.

[0114] A delay-tolerant network caching device based on the max-min ant colony algorithm, comprising:

[0115] Memory: Used to store the computer program that implements the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm;

[0116] Processor: Used to implement the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm when executing the computer program.

[0117] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center of the device using the max-min ant colony algorithm-based delay-tolerant network caching method, connecting various parts of the device through various interfaces and lines.

[0118] When the processor executes the computer program, it implements the steps of the aforementioned delay-tolerant network caching method based on the max-min ant colony algorithm, such as: classifying information; checking whether the cache in the node is full; calculating the total pheromone concentration of the new message; comparing the total pheromone concentration of the new message with the total pheromone concentration of all messages in the current cache; updating the pheromone concentration of the same type of message in the cache; and comparing the updated pheromone concentration of the same type of message with a given pheromone concentration range, thereby implementing the delay-tolerant network caching method based on the max-min ant colony algorithm.

[0119] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, such as: an information classification module: classifying information based on the initial node and the terminal node category; a pheromone concentration calculation module: calculating the pheromone concentration of a single message, the pheromone concentration of messages of the same type, and the total pheromone concentration of messages, limiting them within a specific range, and responsible for updating the pheromone concentration of messages of the same type in the node; a cache management module: managing the cache in the node, including: determining whether the node cache is full, to decide whether a new message should directly enter the cache or need to be discarded after calculating priority before entering the cache; determining the total pheromone concentration of the message, to decide whether to discard a new message or an existing message; and outputting the result of the delay-tolerant network caching method based on the max-min ant colony algorithm.

[0120] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a preset function, wherein the instruction segments describe the execution process of the computer program in the device of the delay-tolerant network caching method based on the max-min ant colony algorithm. For example, the computer program can be divided into an information classification module, a pheromone concentration calculation module, and a cache management module. The specific functions of each module are as follows: Information classification module: classifies information based on the initial node and the terminal node category; Pheromone concentration calculation module: calculates the pheromone concentration of a single message, the pheromone concentration of messages of the same type, and the total pheromone concentration of messages, and limits them within a specific range, and is responsible for updating the pheromone concentration of messages of the same type in the node; Cache management module: manages the cache in the node, including: determining whether the node cache is full, to decide whether a new message should be directly entered into the cache or discarded after calculating priority before entering the cache; determining the total pheromone concentration of messages, to decide whether to discard new messages or existing messages; and outputting the result of the delay-tolerant network caching method based on the max-min ant colony algorithm.

[0121] The device described in the maximum-minimum ant colony algorithm-based delay-tolerant network caching method can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above is an example of a device for a maximum-minimum ant colony algorithm-based delay-tolerant network caching method and does not constitute a limitation on such a device. It may include more components than described above, or combine certain components, or use different components. For example, the device may also include input / output devices, network access devices, buses, etc.

[0122] The memory can be used to store the computer program and / or modules. The processor implements various functions of the device based on the maximum-minimum ant colony algorithm for delay-tolerant network caching by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0123] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0124] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned delay-tolerant network caching method based on the maximum-minimum ant colony algorithm.

[0125] If the system integration module / unit of the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0126] This invention implements all or part of the processes in the aforementioned delay-tolerant network caching method based on the maximum-minimum ant colony algorithm. It can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program implements the steps of the aforementioned delay-tolerant network caching method based on the maximum-minimum ant colony algorithm. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0127] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0128] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0129] It should be noted that embodiments of the present invention can be implemented using hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware.

[0130] Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry of semiconductors such as very large-scale integrated circuits or gate arrays, logic chips, transistors, etc., or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0131] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

[0132] Simulation test

[0133] Simulation parameters and environment:

[0134] In the experiment, we used the ONE simulation platform developed by the University of Helsinki. We divided all the nodes in the network into four categories according to the starting node and the ending node. The specific parameters of the nodes in different categories are shown in Table 1.

[0135] Table 1. Relevant parameters of the simulation experiment

[0136]

[0137] This experiment simulated four algorithms—DF, DO, Ant, and Max-min Ant—under flooding routing and evaluated them based on the following two metrics:

[0138] Message delivery rate: This metric is the ratio of messages successfully delivered to the total number of messages generated in the network.

[0139] Network overhead: This metric is the average number of hops that all messages take in the network.

[0140] Max-min Ant is a delay-tolerant network caching method based on the max-min ant colony algorithm of this invention.

[0141] Analysis of experimental results:

[0142] Buffer size and transmission bandwidth are two factors that significantly affect the metrics. Therefore, this simulation experiment demonstrates the relationship between these two metrics and buffer size and transmission bandwidth, proving that the Max-min Ant algorithm improves the average message arrival rate in the entire network and reduces network overhead compared to the DF, DO, and Ant algorithms.

[0143] See Figure 2 , Figure 3 The four caching methods show similar trends in message arrival rate and network overhead. As the cache size increases, the message arrival rate increases while the network overhead decreases. This is because a larger cache size allows nodes to store more information, increasing the number of replicas of the same message in the network and thus increasing the probability of the message successfully reaching the target node. Simultaneously, a larger cache can hold more messages, reducing the probability of message discarding and the number of retransmissions, thereby reducing network overhead. Compared to the other three existing algorithms, the delay-tolerant network caching method based on the max-min ant colony algorithm proposed in this invention achieves a higher message arrival rate and lower network overhead.

[0144] See Figure 4 , Figure 5 As bandwidth increases, both message delivery rate and network overhead increase. This is because when transmission bandwidth increases, nodes in the network become more active, making it easier for messages to be transmitted, thus resulting in a higher message delivery rate. Furthermore, due to increased activity, messages are transmitted more frequently across the network, increasing network overhead. However, compared to the other three existing algorithms, the delay-tolerant network caching method based on the max-min ant colony algorithm proposed in this invention achieves a higher message delivery rate and lower network overhead.

[0145] See Figure 6 , Figure 7 The proposed delay-tolerant network caching method based on the max-min ant colony algorithm significantly outperforms other mechanisms. This is because, over time, the pheromone concentration of certain types of messages on specific nodes continues to increase, making these nodes more likely to become relay nodes for transmitting certain types of messages, while other types of messages will find it difficult to preempt the cache of these nodes. This makes it less likely for nodes in the network to become congested and drop messages, thereby improving the message delivery rate. Simultaneously, the delay-tolerant network caching mechanism based on the max-min ant colony algorithm effectively solves the problem of ant colony algorithms easily getting trapped in local optima. Over time, the performance of the delay-tolerant network caching mechanism based on the ant colony algorithm converges in approximately 340 minutes, while the delay-tolerant network caching algorithm based on the max-min ant colony algorithm converges in approximately 380 minutes.

[0146] In summary, compared with existing technologies, the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm proposed in this invention has the characteristics of high message arrival rate and low network overhead.

Claims

1. A delay-tolerant network caching method based on the max-min ant colony algorithm, characterized in that, Specifically, the following steps are included: Step 1: Classify the information based on the initial node and the terminal node categories; Step 2: When a new message arrives, check if the cache in the node is full. If the cache is not full, let the new message enter the cache and proceed to step 5; If the cache is full, proceed to step 3; Step 3: Calculate the total pheromone concentration of new messages based on the ant colony algorithm; Step 4: Compare the total pheromone concentration calculated in Step 3 with the total pheromone concentration of all messages in the current cache. If the total pheromone concentration of the new message is at its minimum, discard the new message and proceed to step 5; If the total pheromone concentration of the new message is not the minimum, then discard the message with the minimum total pheromone concentration in the current cache, allow the new message to enter the cache, and proceed to step 5. Step 5: Based on the max-min ant colony algorithm and the node classification in Step 1, update the pheromone concentration of similar messages in the cache according to the classification. ; Step 6: Compare the updated pheromone concentration of similar messages from Step 5 with the given pheromone concentration range. Within the range: when the lower limit of the given pheromone concentration range is ≤ When the value is less than or equal to the upper limit of the given pheromone concentration range, the current caching ends, and the system waits for a new message to arrive. Once the new message arrives, step 2 is executed. Outside the range: When the upper limit is exceeded, the pheromone concentration of the updated message of the same type is taken as the upper limit of the given pheromone concentration range; otherwise, the lower limit of the given pheromone concentration range is taken, and the current caching ends, waiting for a new message to arrive. After the new message arrives, step 2 is executed, and the specific steps are as follows: when When the lower limit of a given pheromone concentration range is reached, Set the lower limit of the given pheromone concentration range, end the current caching, wait for a new message to arrive, and execute step 2 after the new message arrives; when When given the upper limit of the pheromone concentration range, Set the upper limit of the given pheromone concentration range, end the current caching, wait for a new message to arrive, and execute step 2 after the new message arrives.

2. The delay-tolerant network caching method based on the max-min ant colony algorithm according to claim 1, characterized in that, Step 1 specifically includes: During network initialization, all nodes in the entire network are divided into N categories, and information is divided into N*N categories according to the difference between the starting node and the target node, where N is the number of categories of nodes in the network based on node attributes, and N is an integer greater than 1. The node attributes include: node movement speed, node communication range, node movement model, and node communication speed.

3. The delay-tolerant network caching method based on the max-min ant colony algorithm according to claim 1, characterized in that, Step 2 specifically includes: When a new message arrives, compare the size of the new message with the remaining cache size of the node. If the size of the new message is greater than the remaining cache size of the node, the cache is considered full and step 3 is executed; otherwise, the node is considered not full, the new message is allowed to enter the cache, and step 5 is executed.

4. The delay-tolerant network caching method based on the max-min ant colony algorithm according to claim 1, characterized in that, The calculation of the total pheromone concentration of the message in step 3 is specifically as follows: (1) (2) (3) in, This represents the total pheromone concentration of the i-th message at time t. express Entering the node at any time The The pheromone concentration of each message express Entering the node at any time The The remaining lifetime of the message. express Entering the node at any time The The number of times a message is forwarded. For cache utilization, Representing the The size of a message Representative node cache size, This represents the pheromone concentration of similar messages at time t. Recorded in node The cache table is initially set to 0 and then updated in step 5. The cache occupancy rate is the ratio of the message size to the cache size of the arriving node.

5. A delay-tolerant network caching method based on the max-min ant colony algorithm according to claim 1, characterized in that, Step 4 specifically includes: Step 4.1: Use quicksort to sort the nodes. The messages are sorted according to their total pheromone concentration; Step 4.2: If the total pheromone concentration of the new message is less than the total pheromone concentration of the smallest message after sorting in Step 4.1, discard the new message and proceed to Step 5; if the total pheromone concentration of the new message is greater than the total pheromone concentration of the smallest message after sorting, discard the message with the smallest total pheromone concentration in the current cache, allow the new message to enter the cache, and proceed to Step 5.

6. A delay-tolerant network caching method based on the max-min ant colony algorithm according to claim 1, characterized in that, Step 5 specifically includes: Update the pheromone concentration of similar messages in the cache according to the max-min ant colony algorithm: (4) in, This represents the pheromone concentration of similar messages at time t. The decay coefficient representing the historical pheromone concentration. This represents the number of messages of the same category that enter the node at time t. This indicates the lower limit of the pheromone concentration range. This represents the upper limit of the pheromone concentration range, meaning that the pheromone concentration of the same type of message at the current moment is equal to the decrease in the pheromone concentration of the same type of message at the previous moment, plus the sum of the pheromone concentration passing through at the current moment.

7. A delay-tolerant network caching system based on the max-min ant colony algorithm, characterized in that, The method is implemented using the delay-tolerant network caching method based on the maximum-minimum ant colony algorithm as described in any one of claims 1-6, comprising: Information classification module: Used to classify information based on the initial node and the end node category; Pheromone concentration calculation module: Calculates the pheromone concentration of a single message, the pheromone concentration of messages of the same type, and the total pheromone concentration of messages, and limits them to a specific range, and is responsible for updating the pheromone concentration of messages of the same type in the node; Cache management module: Manages the cache in the node, including: Determine if the node cache is full to decide whether new messages should be directly entered into the cache or discarded after priority calculation before entering the cache; The total pheromone concentration of the messages is determined to decide whether to discard new messages or existing messages.

8. A delay-tolerant network caching device based on the max-min ant colony algorithm, characterized in that, include: Memory: for storing a computer program that implements a delay-tolerant network caching method based on the maximum-minimum ant colony algorithm as described in any one of claims 1-6; Processor: configured to implement, when executing the computer program, a delay-tolerant network caching method based on the maximum-minimum ant colony algorithm as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a delay-tolerant network caching method based on the max-min ant colony algorithm as described in any one of claims 1-6.

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